A Novel Pathology Foundation Model by Mayo Clinic, Charit'e, and Aignostics

2025-01-09

AI Summary

创建基于RudolfV方法的新视觉模型，利用120万张细胞组织图片训练，在21个公开测试集上表现最佳，提供病理学研究新方法。

Technology Category

• Computer Vision: Other Foundations of Computer Vision
• Machine Learning: Multi-instance/Multi-view Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods
• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs

Abstract

Recent advances in digital pathology have demonstrated the effectiveness of foundation models across diverse applications. In this report, we present a novel vision foundation model based on the RudolfV approach. Our model was trained on a dataset comprising 1.2 million histopathology whole slide images, collected from two medical institutions: Mayo Clinic and Charit'e - Universt""atsmedizin Berlin. Comprehensive evaluations show that our model achieves state-of-the-art performance across twenty-one public benchmark datasets, even though it is neither the largest model by parameter count nor by training dataset size.

Problem

• Digital Pathology
• Visual Model
• Disease Research

Innovation

• RudolfV Method
• Digital Pathology
• State-of-the-Art Performance